MonogenicJ: A ImageJ plugin for wavelet-based monogenic analysis of images

Outline

The software package MonogenicJ performs multiresolution monogenic analyses of 2D images.
It extracts wavelet-domain features that characterize the local orientation, the phase and the dominant frequency of
an image patch at various levels of resolution.
The package is
available for download as a Java plugin for ImageJ.

MonogenicJ has the ability to compute and display Riesz and Laplace wavelet components, structure-tensor descriptors, and monogenic features
in a dyadic multiresolution framework. It offers two modes of analysis:

pyramid, in which the low resolution wavelet channels are critically sampled;

redundant where all features maps are displayed at the same resolution as the original image.

These are used to build the tensor matrix J which is used to extract the following image descriptors:

Orientation

Coherency

Energy

Note that the tensor matrix is computed for each wavelet cell using a local averaging scheme (Gaussian window).

The next step is the wavelet-domain monogenic analysis which is equivalent to performing a 1D analytic signal analysis along
the dominant orientation. This is achieved via a steering mechanism and leads to the extraction of the following local features.

Download the file MonogenicJ_.jar [Version 21.09.2009] and put it into the "plugins" folder of ImageJ. Restart ImageJ

The whole process should not take more than a couple of minutes.

How to perform the monogenic analysis of an image

Open the input to analyze (only grayscale image).

Launch the plugin MonogenicJ.

Choose the type of the wavelet transformation: dyadic pyramid or full redundant

Select the number of scale J (the width and the height should be a multiple of 2^J).

Choose the size of the weighted window of the structure tensor. It is a Gaussian window defined by its standard deviation σ.

Click on Run.

Display of the features

Select the type the features to display or click on the corresponding Show button.

True values: shows the computed values, in 32-float format.

Scaled values: shows the a rescale [0..255] version of the computed values in each sub-bands.

Stacked presentation: shows the sub-bands as stack of images.

Horizontal Flatten: maps horizontally the sub-bands in one image.

Vertical Flatten: maps vertically the sub-bands in one image.

Color Map: nice way to shows several features at once by assigning at most three features in
color channels. MonogenicJ has a HSB color representation, the hue (H) is often used to display angle (Orientation).
Any computed feature map (in addition to the input image and a constant) can be assigned in the hue (H), saturation (S), or brigthness (B) channels.